Perception-Aware Based Quadrotor Translation and Yaw Angle Planner in Unpredictable Dynamic Situations

In this article, we offer a perception-aware quadrotor translation and yaw angle planner approach to solve the navigation problem for a quadrotor with limited field of view (FOV) in unpredictable dynamic situations. Our method is developed based on the static obstacle avoidance method Ego-Planner. First, we establish an environmental perception module based on a depth sensor. This module categorizes obstacles into static and dynamic types, tracking and predicting the trajectories of dynamic obstacles. Second, we introduce the minimum volume enclosing polynomial curves basis instead of B-spline basis to solve the problem of conservative convex hull generated by existing planning methods based on B-spline basis. To enable dynamic obstacle avoidance, we design a method that combines modified kinematic path searching with gradient optimization, avoiding the need for maintaining a Euclidean signed distance field that adds computational burden in existing approaches. Finally, we jointly optimize translational motion, yaw angle rotation, and the visibility cost of tracking obstacles in the FOV to maximize the unmanned aerial vehicle's ability to observe unknown obstacles early and evade them promptly. Simulation experiments demonstrate that in unpredictable dynamic situations, our proposed planner can effectively avoid obstacles, achieving a high success rate of up to 93%.